In many implementations of machine learning, significant effort may be expended on manually designing features for the purposes of simplifying the learning problem for the classifier. In the case that the inputs are variable-length, feature engineering is generally required to convert the variable-length representation of the raw data into a fixed-length representation that a classifier (e.g., decision trees, logistic regression models, neural networks, etc.) can then use to make decisions about inputs. In this case, the usefulness of the classifier may be almost entirely dependent on the ability of the domain experts to reduce an input of perhaps arbitrary length to a set of fixed descriptive features in a way that maintains predictive power without consuming an inordinate amount of computing resources and/or requiring so long to operate that potential threats cannot be discerned on a usable timescale.